The precise prediction of end-point carbon content in liquid steel plays a critical role in increasing productivity as well as energy efficiency that can be achieved

in the basic oxygen furnace (BOF) steelmaking process. Due to numerous and diversity of the studies on BOF end-point carbon prediction, it seems necessary to provide a comprehensive literature review on state-of-the-art developments in end-point carbon prediction for BOF steelmaking. This paper presents the characteristics of different end-point carbon prediction models. The end-point carbon prediction for BOF steelmaking has initially relied on the experience and skill of the operators. With the development of information technology and autodetection methods, BOF end-point carbon prediction mainly has gone through three stages, such as static prediction, dynamic prediction, and intelligent prediction. Future contributions to the development and application of intelligent

end-point carbon prediction in BOF steelmaking are still arduous tasks. However, it is envisaged that the intelligent end-point carbon prediction will witness more frequent applications and greatly improve the high-quality, high efficiency, and stable production for BOF steelmaking in the future.